Method to identify routes of unmanned aerial vehicles approaching a protected site

ABSTRACT

A system, method, and non-transitory computer readable medium that detects trajectories of unmanned aerial vehicles (UAV) approaching a protected site is described. Airborne defense agents (ADAs) located at a fixed radius from the protected and equidistant from one another detect acoustic signals emitted by an approaching UAV. Circuitry included in each ADA use the detected acoustic signals to determine a direction and a distance of each UAV. A base station having a control center (BS-CC) located in the protected site communicates with the ADAs to aggregate direction and distance data from the ADAs. Using the aggregated direction and distance data, the BS-CC predicts routes towards the protected site of the approaching UAV and alerts the protected site of the predicted route of the approaching UAV.

BACKGROUND Technical Field

The present disclosure is directed to systems and methods for detectingunmanned aerial vehicles.

Description of Related Art

The “background” description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description which may nototherwise qualify as prior art at the time of filing, are neitherexpressly or impliedly admitted as prior art against the presentinvention.

In September 2019, Unmanned Aerial Vehicles (UAVs), also known as“drones”, were used to carry out a terrorist attack on the Saudi Aramcooil field, the largest oil field in the world at the time. As the costof commercially available drones continues to decrease and as theirpopularity continues to increase, high priority has been given todetecting potentially unauthorized UAVs designed to conductmalicious/hostile activities particularly those in the midst ofcommitting such acts. This is especially true for law enforcementagencies. As a UAV can go almost undetected using conventional opticalor electromagnetic sensing techniques, there is great need forinnovation in proactively detecting hostile UAVs and intercepting thetrajectory of an attacking UAV in a cost-effective manner that reducescollateral damage to the surrounding environment.

Some systems currently available for drone detection utilize acollection of sensors, such as radio frequency (RF) sensors and/orultrasonic sensors, and closed-circuit television (CCTV) cameras todetect drone activity. DRONEDNA, offered by Dedrone of Dallas, Tex., isone specific example of such a system. However, DRONEDNA and othersimilar systems suffer from poor night vision, a limited area ofcoverage, and slow response time. Other systems for detecting UAVscurrently available, such as SKYLOCK, offered by the Avnon Group ofPetah Tikva, Israel, provide a multi-layered platform of modular systemsfor the detection, verification, and neutralization of unauthorized UAVsthrough a combination of passive and active systems including RF and/orinfrared (IR) radar systems. However, SKYLOCK and similar systems facelimitations based on the signals being emitted by a suspectedunauthorized UAV, the materials of which the UAV is made, and the systembeing only semi-automated. SKYLOCK and similar systems also suffer froma limited range of coverage.

Responsive to the detection of an approaching drone by systems likeDRONEDNA/SKYLOCK, a determination is made as to whether the approachingdrone has authorization to be in the air space and an assessment is madeas to the threat level of the approaching drone. If the approachingdrone is not authorized to be in the air space and/or is deemed to be asufficient threat, efforts are made to neutralize the approaching drone.

Currently, there are three primary methods to neutralize drones:jamming, hacking, and destroying. Typically, jamming entails disruptinga drone by generating a signal (e.g., electromagnetic, RF, ultrasonic,etc.) having a power level sufficient to interfere with, or “drown out”any communications between the drone and its pilot. Readily availableproducts, such as an RF drone signal jammer, emit signals strong enoughto interfere with the communications between a drone and its pilot.However, these jammers are limited in the event the drone operatesautonomously or in a mode that neither emits nor receiveselectromagnetic signals of any kind from an external source in order tooperate.

Hacking typically entails taking control of a drone directly.Conventional techniques for hacking may include “spoofing” controlsignals in order to trick the drone into thinking it is being controlledby the intended operator. However, successfully hacking a drone presumesadequate knowledge of control/communications protocols and/or commandsused in operating the drone.

Destroying a UAV entails using physical force sufficient to destroy theapproaching drone. While commercially available drones such as thePHANTOM 4 PRO, offered by DJI of Shenzhen, China are easilydestructible, military-grade UAV are not. In fact, destroying amilitary-grade UAV may necessitate the use of military-grade defensesystems, such as Patriot and/or Hawk missile systems, which areextremely expensive and are prone to damaging the surroundingenvironment.

To go undetected by DRONEDNA/SKYLOCK or other similar systems, UAVs madeof irreflective material, that emit very small signals, and/or that flyat night can be produced. Therefore, there is a need for a systemcapable of detecting an unauthorized UAV regardless of composition, thestrength of emitted signals (e.g., RF, ultrasonic, electromagnetic,etc.), the time of day at which the drone is flown, or othercharacteristics intrinsic to the UAV that the system is trying todetect. To overcome these technical limitations and produce a dronedetection system that is cost-feasible and that minimizes collateraldamage to the surrounding environment, advances in the field ofdrone-detection technology are needed.

Accordingly, it is an object of the present disclosure to providemethods and systems for a distributed airborne acoustic anti dronesystem which can detect a UAV approaching a protected site, predict aroute or routes toward the protected site which the approaching UAV maytraverse, and alert the protected site of the predicted route(s) of theapproaching UAV.

SUMMARY

In an exemplary embodiment, a distributed airborne acoustic anti-droneintelligence system (DAAADS) for detecting trajectories of an unmannedaerial vehicle (UAV, or “drone”) approaching a protected site isdescribed. The DAAADS includes multiple airborne defense agents (ADAs),each located at a fixed distance/radius from the protected site andequidistant from each other ADA. Also included in the system is a basestation including a wideband communications line configured tocommunicate with a transceiver included in each ADA and the protectedsite. A control center located within the base station (BS-CC) isprogrammable to aggregate data corresponding to the directions anddistances of an approaching UAV, to predict a route or routes toward theprotected site which the approaching UAV may traverse, and to alert theprotected site of the predicted route(s) of the approaching UAV.

In another exemplary embodiment, a method includes detecting, via ADAs,trajectories of UAVs approaching a protected site. Detection is achievedby switching each directional microphone included in a directionalmicrophone array of each ADA on and off during consecutive time periods.Only one directional microphone is on in any given time period. Duringon periods, acoustic signals generated by UAVs approaching the protectedsite are detected.

Circuitry included in each ADA estimates an angle of approach and adistance of each approaching UAV from each ADA over a first and a secondtime period and transmits these estimates to a base station. Using theestimated distance, circuitry included in the base station divides bythe difference between the first and the second time period to estimatea speed of each UAV. The angles of approach, distances, and speeds ofthe approaching UAVs are aggregated to predict routes towards theprotected site. An alarm is transmitted to the protected site when thepredicted route of at least one approaching UAV intersects with theprotected site.

In another exemplary embodiment, a non-transitory computer readablemedium having instructions stored thereon that, when executed by one ormore processors, cause the one or more processors to perform a methodfor detecting trajectories of unmanned UAV approaching a protected site,the site being protected by multiple ADAs. To detect the trajectories ofthe approaching UAV, each directional microphone of a directionalmicrophone included in each ADA is switched on and off duringconsecutive time periods. Only one directional microphone is on in agiven time period. Acoustic signals generated by the approaching UAVsare detected during consecutive on periods.

During a first time period, a first circuitry included in an ADAestimates an angle of approach and a distance of approach of eachapproaching UAV from each ADA. During a second time period, the firstcircuitry estimates an angle of approach and a distance of eachapproaching UAV form each ADA. Estimated angle of approach and distancefrom each ADA are transmitted to a base station.

A second processing circuitry, included in the base station, estimates aspeed of each UAV by subtracting the distance estimated during a firston time period from the distance measured during a second on time periodfor each of three equidistance ADAs and dividing by the difference bythe difference between the first and second time periods. Angles ofapproach, distances, and speeds of the approaching UAVs are aggregatedto predict routes towards the protected sites of the approaching UAVs.An alert is transmitted to the protected site when the route of at leastone of the approaching UAV intersects with the protected site.

The foregoing general description of the illustrative embodiments andthe following detailed description thereof are merely exemplary aspectsof the teachings of this disclosure, and are not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of this disclosure and many of theattendant advantages thereof will be readily obtained as the samebecomes better understood by reference to the following detaileddescription when considered in connection with the accompanyingdrawings, wherein:

FIG. 1 is an illustration of a distributed airborne acoustic anti-droneintelligence system, according to certain embodiments.

FIG. 2 is an illustration of a distributed airborne acoustic anti-droneintelligence system surrounding a protected site, according to certainembodiments.

FIG. 3 illustrates tethering an airborne defense agent to a tower,according to certain embodiments.

FIG. 4 is an exemplary flowchart of a method for implementing adistributed airborne acoustic anti-drone intelligence system, accordingto certain embodiments.

FIG. 5 illustrates a microphone pattern for predicting the direction ofan incoming attack on a protected site, according to certainembodiments.

FIG. 6 is an exemplary illustration of an acoustic signal emitted by anapproaching attacker, as detected by an airborne defense agent,according to certain embodiments.

FIG. 7 illustrates position triangulation of an attacking agentapproaching a protected site by airborne defense agents surrounding theprotected site, according to certain embodiments.

FIG. 8 illustrates measurement of a propagation delay of an acousticsignal emitted by an attacking agent, according to certain embodiments.

FIG. 9 depicts a process by which an angle of arrival of an attackingagent approaching a protected site is estimated, according to certainembodiments.

FIG. 10 illustrates determination, by a group of airborne defenseagents, of the speed of an attacking agent approaching a protected site,according to certain embodiments.

FIG. 11 illustrates an exemplary determination of the number ofattacking agents approaching a protected site by airborne defenseagents, according to certain embodiments.

FIG. 12 illustrates examples of multiple trajectories an attacking agentmay traverse while approaching a protected site, according to certainembodiments.

FIGS. 13A-13E illustrate multiple ways in which groups of airbornedefense agents may detect an attacking agent approaching a protectedsite, according to certain embodiments.

FIG. 14 is an illustration of a non-limiting example of details ofcomputing hardware used in the computing system, according to certainembodiments.

FIG. 15 is an exemplary schematic diagram of a data processing systemused within the computing system, according to certain embodiments.

FIG. 16 is an exemplary schematic diagram of a processor used with thecomputing system, according to certain embodiments.

FIG. 17 is an illustration of a non-limiting example of distributedcomponents which may share processing with the controller, according tocertain embodiments.

DETAILED DESCRIPTION

In the drawings, like reference numerals designate identical orcorresponding parts throughout the several views. Further, as usedherein, the words “a,” “an” and the like generally carry a meaning of“one or more,” unless stated otherwise.

Furthermore, the terms “approximately,” “approximate,” “about,” andsimilar terms generally refer to ranges that include the identifiedvalue within a margin of 20%, 10%, or 5%, and any values therebetween.

Aspects of the present disclosure are directed to a system, device, andmethod for a distributed airborne acoustic anti-drone intelligencesystem (DAAADS) which is a fully automated, dynamic, scalable, remote,intelligent, and long-range distributed system for detectingtrajectories of an unauthorized, potentially malicious/hostile UAVapproaching a protected site. By utilizing (e.g., sensing) acousticsignals and generating a reliable stream of data from those signals, theDAAADS is able to intercept the UAV approaching the protected site. Insome examples, “generating a reliable stream of intelligence” mayinclude using sensed acoustic data to calculate a speed/angle ofapproach of a detected UAV, and predicting possible trajectories of thedetected UAV based on the calculated speed/angle of approach. Thepredicting possible trajectories of the detected, approaching UAV may beperformed by circuitry included in the ADAs comprising the DAAADS, thebase station, the BS-CC located in the protected zone, or in anycombination thereof.

FIG. 1 depicts a distributed airborne acoustic anti-drone intelligencesystem (DAAADS) 100 for detecting trajectories of unmanned aerialvehicles (UAVs) approaching a protected site in accordance withembodiments of the present disclosure. Airborne Defense Agents 110(1),110(2), 110(3), . . . , 110(n) (collectively, “the ADAs 110”) are shownsurrounding a protected site 120. Included in the protected site 120 isa base station 130 having a control center (a BS-CC) 132 that is incommunications with the ADAs 110. Communications between the ADAs 110and the BS-CC 132 may be enabled via any number of wirelesscommunications protocols including, but not limited to Bluetooth, WiFi,2G/3G/4G/LTE/5G, ZigBee, NFC, RFID, and all variants thereof. Alsodepicted in FIG. 1 are attacking agents 140(1), 140(2), and 140(3)(collectively “the AAs 140”). The AAs 140 are unmanned aerial vehicles(UAVs, or drones) approaching the protected site 120. In variousembodiments, the AAs 140 lack authorization to be in the protected site120 and/or may be hostile UAVs having malicious intent.

Each of the ADAs 110 is located at a fixed radius from protected site120 and equidistant from each other ADA of the ADAs 110. Each of theADAs 110 is equipped with a microphone array (shown in FIG. 3)configured to detect acoustic signals emitted by a UAV.

Included in each of the ADAs 110 is a computing device (also shown inFIG. 3) including a computer-readable medium comprising programminginstructions executable by processing circuitry. Responsive to executionof the programming instructions, the processing circuitry determines adirection and a distance of each of the AAs 140 from each of the ADAs110. In various embodiments, the computing device includes switchingcircuitry configured to switch the power of each directional microphoneON and OFF in an alternating sequence. In accordance with the presentdisclosure, each directional microphone of the directional microphonearray may be oriented to receive acoustic signals from a differentangle. Moreover, each computing device may include circuitry configuredto measure an amplitude, A, of each acoustic signal during thealternating ON periods.

Each processing circuitry of the ADAs 110 may be further configured todetect an angle of arrival, ω, of the acoustic signal from the angle ofthe directional microphone receiving the greatest amplitude. Eachprocessing circuitry of the ADAs 110 can then estimate the distancebetween each of the ADAs 110 and each of the AAs 140 by measuring apropagation delay, τ, of the acoustic signal having the greatestamplitude. Moreover, the processing circuitry may be configured toconvert the acoustic signals from the time domain to the frequencydomain, identify a set of frequency components, and estimate thedistance to the sound source.

Each of the ADAs 110 may also include GPS receiver circuitry (314, shownin FIG. 3) along with (wireless) transceiver circuitry (315, FIG. 3).The GPS receiver circuitry can be configured to locate the respectiveADA of the ADAs 110 in a three-dimensional space. Each of the ADAs 110may further include a motor, a propeller, and navigation circuitry (318,319, 316, FIG. 3) configured to cause a respective ADA of the ADAs 110to hover at a fixed height from the ground, at a fixed radius from theprotected site, and equidistant from each other ADA of the ADAs 110.

The DAAADS 110 further includes a BS-CC 132 configured with a widebandcommunications link 134 to communicate with the transceiver of each ofthe ADAs 110 and protected site 120. In embodiments, the BS-CC 132 maybe configured to transmit position signals to the navigation circuitryincluded in each of the ADAs 110 to control the position of a respectiveADA of the ADAs 110. The BS-CC 132 may be further configured via acomputing device 136 including a computer-readable medium comprisingprogram instructions, executable by processing circuitry, to cause theprocessing circuitry to aggregate the directions and distances ofapproaching AAs 140 to predict routes towards the protected site and toalert the protected site of the predicted route of each of the AAs 140while approaching protected site 120. The computing device 136 may be apersonal computing device, a laptop, a mainframe, a server, a database,or any combination thereof.

In accordance with the present disclosure, the processing circuitry ofthe BS-CC 132 may be configurable to transmit control signals to each ofthe ADAs 110 to switch the directional microphone arrays includedtherein ON and OFF simultaneously. Moreover, the second processingcircuitry may be further configurable to predict the route of arespective AA of the approaching AAs 140 by triangulating the acousticsignals received by the directional microphone arrays of threeequidistant ADAs of the ADAs 110. Further, the second processingcircuitry may also include a machine learning processor configured topredict the route of each of the approaching AAs 140 toward protectedsite 120. The second processing circuitry may also be configured toestimate the speed of each of the AAs 140 by subtracting the distancesestimated by each of the three equidistant ADAs of the ADAs 110 at afirst ON period from the distances estimated at a second ON period anddividing the difference by the time between the first ON period and thesecond ON period.

FIG. 2 illustrates the taxonomy of protected region 200, a region whichmay be protected by a DAAADS substantially similar/identical to DAAADS100. Included in protected region 200 is protected site 210. Protectedsite 210 may be substantially similar/identical to protected site 110described above. Protected region 200 also includes a demilitarized zone(DMZ) 220, a detection/reaction (D/R) zone 230, and a sensing zone 240.Airborne defense agents 250(1), 250(2), 250(3), . . . , 250(n)(collectively “the ADAs 250”) surround the perimeter of the DMZ 220 andthe D/R zone 230, and detect approaching attacking agents 260(1),260(2), and 260(3) (collectively “the AAs 260”).

Included in protected site 210 are a variety of communicationsfacilities, synchronization facilities, and computation facilities and abase station (which may be substantially similar/identical to basestation 130), and included in the base station is a control center, orBS-CC (which may be substantially similar/identical to BS-CC 132). TheBS-CC is programmable to communicate with the various communicationsfacilities, synchronization facilities, and/or computation facilitiesthroughout protected site 210 and the ADAs 250 to aggregate data on theAAs 260 approaching protected site 210. Communications between the BS-CCand the facilities/the ADAS 250 may be enabled via a variety of wirelesscommunications protocols including Bluetooth, WiFi, 2G/3G/4G/LTE/5G,ZigBee, NFC, RFID, and all variants thereof. The aggregated data maycorrespond to the directionality and distance of an approaching AA ofthe AAs 260 from protected site 210. The aggregated data may be usedcalculate the speed of the AAs 260 and/or to predict a route/trajectoryof a respective AA of the AAs 260 on approach. A threat level ofapproaching AAs 260 may be assessed based on these calculations. Basedon the assessed threat level, alarms may be sounded throughout protectedsite 210.

The coordinates of each individual ADA of the ADAs 250 are known, andeach individual ADA is used to sense a specific area. In someembodiments, the ADAs 250 may be air balloons. In other aspects, theADAs 250 may be blimps, helicopters, drones, or any other type ofaircraft able to hover in or otherwise maintain a relatively steadyposition. Each ADA may include a directional microphone array and/or anelectronically driven loop forming directional microphone systemconfigured to sense acoustic signals emitted from UAVs (e.g., AAs 260)approaching protected site 210. Each ADA may also include a variety ofother sensors/circuitry, such as a frequency analyzer, a power meter,timers, and GPS/navigational circuitry configured to measure/aggregatedata related to the speed/position (e.g., distance from) relative toprotected site 210. Each ADA may also include computational circuitryconfigured to calculate the speed and/or position of an approaching UAVand predict trajectories of the approaching UAV therefrom. Thetrajectories may be estimated using any machine learning algorithmincluding, but not limited to, supervised/unsupervised/semi-supervisedlearning algorithms, regression-based algorithms, clustering algorithms,Bayesian algorithms, decision tree algorithms, regularizationalgorithms, instance-based algorithms, association rule learningalgorithms, artificial neural network algorithms, deep learningalgorithms, or any combination thereof.

In some aspects of the present disclosure, and as is covered greaterdetail in the ensuing discussion of FIG. 3, each of the ADAs 250 may behovering in a fixed position. To fix the ADAs 250 in place, each ADA maybe tethered (e.g. via a rope or chain) to a communications tower orother structure configurable to facilitate wireless communications. Inother embodiments, each of the ADAs 250 may hover above a communicationstower, but not be restricted by a tether. In still other embodiments,each of the ADAs 250 or a combination of individual ADAs may beuntethered and able to fly around protected region 200. In theseinstances, each of the untethered ADAs may be able to fly aroundprotected region 200 freely, or may fly in a fixed formation.

Sensing zone 240 surrounds the D/R Zone 230, and is the outermostsection of protected region 200. The AAs 260 are depicted as flying insensing zone 240 approaching protected site 210. While the AAs 260 arein sensing zone 240, intelligence is gathered (e.g., emitted acousticsignals are measured) in order to categorize behavior and to predicttrajectory. Predictions as to behavior and trajectory of the AAs 260 aremade by the BS-CC included in protected site 210. As mentioned above,each of the ADAs 250 included in the DAAADS may be hovering around aprotected site in a fixed position.

FIG. 3 illustrates an ADA 310 connected to a tether 320, having length,L. The tether 320 is connected is connected to a tower 330 havingheight, H. In some embodiments, the DAAADS may include a plurality oftowers 330 of height H throughout a protected region. Each tower 330 maybe located a fixed radius from the protected site and equidistant fromeach other tower. A top portion of each fixed tower 330 is connected toone of a plurality of tethers 320 (e.g., ropes, chains, etc.), eachtether 320 of length L. Each ADA may include an air balloon 312, eachair balloon having a lower mount, may be attached via the lower mount totether 320. Each air balloon is configurable to hold the first computingdevice and the directional array of an ADA 310 at a fixed height L+Habove the ground.

Also depicted in FIG. 3 and as referenced throughout the descriptions ofFIG. 1 and FIG. 2 are directional microphone array 311, criticalcircuitry 312 motor 318, and propeller 319. In accordance with thepresent disclosure, a directional microphone array 311 senses acousticsignals emitted from an approaching attacking agent. Included incritical circuitry 312 are a computing device 313 executing code fordetermining a direction and a distance of each UAV approaching ADA 310,a GPS receiver 314 configured to locate the ADA in a three-dimensionalspace, and transceiver circuitry 315 for sending/receiving wirelesscommunications signals. Motor 318 may drive propeller 319 such that ADA310 is able to hover at a fixed height of H+L above the ground. Criticalcircuitry 312 may further include navigation circuitry 316. Navigationcircuitry 316 may include the GPS receiver 314 or it may providenavigation capabilities in addition thereto.

FIG. 4 is a flowchart describing a method 400 for detecting trajectoriesof unmanned aerial vehicles (UAV) approaching a protected site by aplurality of airborne defense agents (ADAs). The method 400 includes, ata step 410, switching each directional microphone of a directionalmicrophone array of an ADA ON and OFF during consecutive time periods inwhich only one directional microphone is ON in a time period. At step420, acoustic signals generated by UAVs approaching the protected siteduring consecutive ON periods are detected. A first processing circuitryof an ADA estimates an angle of approach and a distance of eachapproaching UAV from each ADA during the first time period at step 430.At a step 440, during the second time period, an angle of approach anddistance of each approaching UAV from each ADA is estimated.

The estimated angle(s) of approach and distance(s) from each ADA aretransmitted to a base station at step 450. A second processing circuitryincluded in the base station estimates, at a step 460, a speed of eachUAV. The estimating may include subtracting the distance estimatedduring a first ON time period from the distance measured during a secondON time period for each of three equidistant ADAs and dividing by thedifference between the first and second time periods. At step 470, theangles of approach, distances, and speeds of the approaching UAVs areaggregated to predict routes towards the protected site. At step 480, analert is transmitted to the protected site when the route of at leastone approaching UAV intersects with the protected site.

In some aspects of the present disclosure, the base station may performan optional step 405 to transmit to each ADA navigation signals tocontrol the ADAs to hover at a fixed height from the ground, at a fixedradius from the protected site, and equidistant from each other ADA.Additionally and/or alternatively, the method 400 for detectingtrajectories of unmanned aerial vehicles (UAV) approaching a protectedsite may include an optional step 408 for transmitting control signalsfrom the base station to each ADA to switch each directional microphonearray ON to start detecting acoustic signals or OFF to sleep based on anumber of approaching UAVs.

The first processing circuitry of each ADA may convert the acousticsignals from the time domain to the frequency domain. Any signalprocessing technique known in the art may be used to perform theconversion from the time domain to the frequency domain. Responsive toconversion into the frequency domain, a set of frequency components isidentified, and distance to an approaching UAV is estimated using theset of frequency components.

Each directional microphone of the directional microphone array of anADA may be oriented to receive acoustic signals from a different angle.The first processing circuitry may measure an amplitude, A, of eachacoustic signal during the alternating ON periods. An angle of arrival,ω, of the acoustic signal may be detected from the angle of thedirectional microphone receiving the greatest amplitude. Estimates ofthe distance between each ADA and a UAV may be made by measuring apropagation delay, τ, of the acoustic signal having the greatestamplitude.

The second processing circuitry may predict the route of an approachingUAV by triangulating the acoustic signals received by the directionalmicrophone arrays of three equidistant ADAs. Further, the secondprocessing circuitry may apply the aggregated angles of approach,distances, and speeds of the approaching UAVs to a machine learningprocessor to predict the routes of the approaching UAVs. In someembodiments, the machine learning processor may be included in the BS-CCand may implement a machine learning algorithm to predict theroutes/trajectories of the approaching UAV. Examples of the machinelearning algorithms include, but are not limited to,supervised/unsupervised/semi-supervised learning algorithms,regression-based algorithms, clustering algorithms, Bayesian algorithms,decision tree algorithms, regularization algorithms, instance-basedalgorithms, association rule learning algorithms, artificial neuralnetwork algorithms, deep learning algorithms, and/or a combinationthereof.

In related aspects, a non-transitory computer readable medium havinginstructions stored thereon that, when executed by one or moreprocessors, cause the one or more processors to perform a method fordetecting trajectories of unmanned aerial vehicles (UAV) approaching aprotected site by a plurality of airborne defense agents (ADAs). Themethod may include switching each directional microphone of adirectional microphone array of an ADA ON and OFF during consecutivetime periods in which only one directional microphone is ON in a timeperiod. Acoustic signals generated by UAVs approaching the protectedsite may be detected during consecutive ON periods. A first processingcircuitry of an ADA may estimate an angle of approach and a distance ofeach approaching UAV from each ADA during the first time period.Furthermore, an angle of approach and distance of each approaching UAVfrom each ADA may be estimated during the second time period.

The estimated angle(s) of approach and distance(s) from each ADA may betransmitted to a base station. A second processing circuitry included inthe base station may estimate a speed of each UAV by subtracting thedistance estimated during a first ON time period from the distancemeasured during a second ON period for each of three equidistant ADAsand dividing by the difference between the first and second timeperiods. Distances and speeds of the approaching UAVs are aggregated topredict routes towards the protected site of the approaching UAV.Finally, an alert may be transmitted to the protected site when theroute of at least one approaching UAV intersects with the protectedsite.

The non-transitory computer readable medium may also orient eachdirectional microphone of the directional microphone array of an ADA toreceive acoustic signals from a different angle. Processing circuitry ofthe ADA may be used to determine an amplitude (A) of each acousticsignal during the alternating ON periods. Further, the processingcircuitry may also detect an angle of arrival (ω) of the acoustic signalbased on the orientation of the directional microphone array sensing theacoustic signal having the largest amplitude. Moreover, the processingcircuitry may estimate the distance between the ADA and an approachingUAV by measuring a propagation delay (τ) of the acoustic signal havingthe greatest amplitude.

The non-transitory computer medium may also predict, by the secondprocessing circuitry included in the base station, the route/trajectoryof each approaching UAV by triangulating the acoustic signals receivedby the directional microphone arrays of three equidistant ADAs. Thesecond processing circuitry may apply aggregated angles of approach,distances, speeds, and predicted routes of the approaching UAVs to amachine learning processor to predict the routes which intersect withthe protected site. To predict the routes of the approaching UAV, themachine learning processor may implement any machine learning algorithmincluding, but not limited to, supervised/unsupervised/semi-supervisedlearning algorithms, regression-based algorithms, clustering algorithms,Bayesian algorithms, decision tree algorithms, regularizationalgorithms, instance-based algorithms, association rule learningalgorithms, artificial neural network algorithms, deep learningalgorithms, and/or a combination thereof.

FIGS. 5-12 provide more detailed descriptions of the functionality of(e.g., the various tasks executed by) the DAAADS in detecting a UAVapproaching a protected site. It is to be understood that there may bemultiple different ways to perform these tasks in addition to what isdescribed in FIGS. 5-12, and/or there may be multiple iterations forperforming what is described.

FIG. 5 illustrates one process by which DAAADS may predict the directionof an incoming attack from an attacking agent (AA) approaching protectedregion 510, having center, C, and radius, r. For the purposes ofpredicting direction of an incoming attack, points x, y, and b aroundthe circumference of protected region 510 are of interest, as is pointa. As illustrated, an airborne defense agent (ADA) is located at point band the AA is located at point a. Microphone pattern 420 is indicativeof the acoustic signal emitted by the AA at point a and sensed by ADA atpoint b. Based on microphone pattern 520, the initial coordinate of AAis estimated.

Estimating the initial position of the approaching AA may initiate anearly alarm, the alarm indicating that an imminent threat may beapproaching protected region 510 from a specific direction. The lengthof line segment ab, or the in a straight line between the AA at point aand the ADA at point b, may be calculated based on the propagation delayof the sound originating at the AA multiplied by the speed of sound(approximately 343 m/s).

To improve estimates as to the location of the AA, three points aroundthe protected region 510 may be used to triangulate the position of theAA. As illustrated, points x, y, and b are these three points. Initiallythe locations (e.g., coordinates) of points b and a are known, and areused in determining the locations of points x and y. First, tangents tothe protected region 510, one above and one below the known location theADA (e.g., point b) passing through the known location of the AA (e.g.point a), are taken. The intersections of these tangent lines withprotected region 510 are the points x and y. Additionally and/oralternatively, by approximating θ≈60°, the coordinates of x and y can beapproximated. After establishing the three fixed points, x, y, and b, itis possible to estimate the coordinates of the attack in athree-dimensional space via triangulation using the edges of the righttriangles (e.g., ΔCXA and ΔCYA).

FIG. 6 illustrates the sound components involved in an ADA 620sensing/detecting an approaching AA 610. As sound waves 615 are emittedfrom the approaching AA 610, the sound waves 615 will be detectable bythe array of microphones in ADA 620 once the sound waves 615 haveentered the sensing zone of the microphone array. The sensing zone ofthe microphone array is indicated by a microphone pattern 625 which maybe substantially similar/identical to the microphone pattern 520. Totrack the approaching AA 610 as accurately as possible, the lowfrequency components of sound waves 615 are monitored. Tracking thelower frequency components of sound emitted from approaching AA 610 ismore effective. Given the longer wavelength of lower frequency soundwaves, lower frequency components of sound waves 615 will propagatefurther away from their source (e.g., AA 610) than the higher frequencycomponents of sound waves 615.

The AA 610 can be detected by sounds generated (e.g., by its motor,propeller, wind impacting it body), which serve as the origin of soundwaves, depicted as 615, which traverse the sound range shown in FIG. 6.The ADA 620 has a sensing range corresponding to microphone pattern 625in which incoming sounds can be sensed. As sound waves 615 cross intomicrophone pattern 625, the frequency spectrum of sound waves 615 can beanalyzed. The ADA 620 may transmit data collected about sound waves 615to a BS-CC substantially similar/identical to the BS-CCs described above(e.g., BS-CC 132) for analysis.

FIG. 7 provides further detail on the localization/triangulation of anAA approaching a protected site by three ADAs. Three ADAs, 710(1),710(2), and 710(3) (collectively “the ADAs 710”) are shown detectingapproaching AA 720. To triangulate the position of AA 720 (e.g., findthe coordinates of AA 720 in a three-dimensional space), the ADAs 710sense acoustic signals emitted by AA 720. Responsive to sensing theacoustic signals, each ADA of the ADAs 710 establishes a relativelocation by determining a propagation delay (τ) and angle of arrival (ω)of the sensed acoustic signals using the array of directionalmicrophones included in each respective ADA of the ADAs 710. Propagationdelays are measured while angles of arrivals are estimated.

FIG. 7 illustrates each respective ADA of the ADAs 710 having a measuredpropagation delay and an estimated angle of arrival of the sensedacoustic signal emitted from attacking agent (AA) 720. For example, ADA710(1), located at point a, uses the acoustic signal(s) emitted from AA720 to measure a propagation delay τ₁ and to estimate an angle ofarrival ω₁ of AA 720. Similarly, ADA 710(2), located at point b,measures a propagation delay (τ₂) and estimates an angle of arrival (ω₂)of the acoustic signals emitted from AA 720, and ADA 710(3), located atpoint b, measures a propagation delay (τ₃) and estimates an angle ofarrival (ω₃) of the acoustic signals emitted from AA 720. A combinationof these measurements and estimations may be used to triangulate theposition of AA 720 as it approaches. Lengths of the sides of thetriangles Δabd and Abcd formed between the ADAs 710 and AA 720 (e.g.,the line segments ad, bd, and cd) may be used to calculate the positionof AA 720 in a three-dimensional space.

Although FIG. 7 depicts three ADA (710(1), 710(2), and 710(3))triangulating the position of AA 720 as it traverses distance x, inaccordance with the present disclosure, it is possible to triangulatethe position of AA 720 with four, five, ten or more ADA depending onvarious factors, including but not limited to the trajectory of theapproaching AA and the formation of the ADA. Provided there are norestrictions/limitations caused by system overhead (e.g., insufficientbandwidth), all ADA in included in the DAAADS may track an approachingAA at once. In general, the more ADA that are triangulating the positionof an approaching AA, the more accurate the results will be.

FIG. 8 illustrates measurement of propagation delay, or the timerequired for a sound to travel from the source/origin of the sound to alocation/object sensing that sound. In accordance with the presentdisclosure, an attacking agent emitting acoustic signals serves as thesource/origin and an airborne defense agent sensing acoustic signalsemitted from an attacking agent serves as the sensing object.Synchronizing ADAs to a reference channel enables the ADAs to listen tothe same acoustic signals at the same time, enabling propagation delay,τ, to be measured. Propagation delay may be measured by switching ON andOFF microphone arrays included in the ADAs simultaneously andperiodically.

As illustrated in FIG. 8, an ADA may measure τ during a first ON cycle(ON[1]). The acoustic signal emitted from an AA is sensed afterpropagating for r seconds, but there is no way to distinguish thepropagation time from the origination time. To resolve this issue, ameasurement of τ is made during a second ON cycle (ON[2]) to start theON cycle. Minimum and maximum values of τ may be identified according tosystem specifications and any measured delay should fit within thatrange. For instance, the maximum value for r may be based on the maximumsensing range of the system while the minimum value of τ may be based onthe sensitivity of the measurement device(s) (e.g., the microphone arrayincluded in each ADA(s) sensing the emitted acoustic signal(s)). Alsoillustrated in FIG. 8 is the ADA receiving the emitted acoustic signal,after the propagation delay τ, and during each of the first and secondON cycles.

FIG. 9 depicts a process by which an angle of arrival (co) may beestimated. The array of microphones included in an ADA (e.g., ADA 910)has three loops, loop 1 (920(1)), loop 2 (920(2)), and loop 3 (920(3))(collectively “the loops 920”), each separated by 60°. The angle ofarrival is associated with the loop in which the strongest acousticsignal is received. For instance, if the strongest acoustic signal issensed within loop 1 (920(1)), angle of arrival is estimated to be +60°.To associate the received acoustic signal to any of the loops, ADA 910may switch the power between the loops in an alternating fashion,thereby enabling ADA 910 to determine which of the loops 920 isreceiving the strongest signal (e.g., the signal having the highestpower). To improve the accuracy of the estimation of the angle ofarrival, more loops may be included. More loops may be included bynarrowing/decreasing the beam width 930 of any/all of the loops 920 ofthe microphone array. Decreasing the beam width of the loops 920 alsoserves to reduce interference between individual loops of the microphonearray.

FIG. 10 illustrates the determination by ADAs 1010(1), 1010(2), and1010(3) (collectively “the ADAs 1010”) of the speed of AA 1020. As shownin the figure, AA 1020 has traversed a distance x on its trajectory at acertain speed. By determining the distance x that AA 1020 has travelledbetween two detection times, the ADAs 1010 can determine the speed atwhich AA 1020 is travelling.

The distance x is the distance between two localization points, and iscalculated by triangulating the position of AA 1020 at two consecutiveinstances of time (using the propagation delay (τ) and an angle ofarrival (ω) estimated by the ADAs 1010 and a second localization pointbeing triangulated by the ADAs 1010). The first localization point iscalculated by the ADAs 1010, as described above and below. Thelocalization points are measured according to a localization frequency(f_(lo)), which may equal 1/T, where T is approximately the timeinterval between a first at time t₁ and the second localization point iscalculated at time t₂, then T=t₂−t₁. Thus, as f_(lo) is the inverse ofT, a higher localization frequency value reflects the system localizingthe approaching AA more often than at a lower localization frequency.Upon configuring the system, T may be chosen and the distance x can beapproximated as a straight line. Accordingly, the calculated values ofdistance x and f_(lo) can be used to determine the speed, s, of AA 1020by multiplying x by f_(lo)(s=xf_(lo)).

In accordance with the present disclosure, a speed profile, including aminimum speed S_(min) and a maximum speed S_(max) for an AA approachinga protected site may be constructed to reflect the change in speed ofthe approaching AA. Accuracy of the profiling is limited by thesegmentation resolution, and the speed profile may be used in estimatingpossible trajectories of an approaching AA.

An AA approaching a protected site may traverse a fixed trajectory. Inaccordance with the present disclosure, a speed profile, including aminimum speed (S_(min)) and a maximum speed (S_(max)) for an AAapproaching a protected site may be constructed to reflect the change inspeed of the approaching AA as it travels along the trajectory. Togenerate a speed profile for an approaching AA, the DAAADS may vary thelocalization frequency of the ADAs included therein. Varying thelocalization enables the DAAADS to determine a maximum and a minimumspeed for the approaching AA between two consecutive localization pointsover a given time interval, and to predict, from the determination,possible trajectories the approaching AA may follow.

FIG. 11 depicts an exemplary determination of numbers of approachingattacking agents may be counted by airborne defense agents. A microphonearray included in ADA 1110(1), 1110(2), and 1110(3) (collectively “theADAs 1110”) generate loops. The microphone array of each of the ADAs1110 generates loop a, loop b, and loop c. To count the number ofapproaching AAs, one loop per ADA at a time receives the acoustic signalwith the highest power (P_(h)) emitted from an AA approaching aprotected site. Associating an attacking agent, such as AA 1120(1)and/or 1120(2) (collectively, “the AAs 1120”) to a single loop of theloops emitted by the microphone arrays of the ADAs 1110 reducesconflicts arising from potential inaccuracies in propagation delaysmeasured and/or angles of arrival estimated by the ADAs 1110 in countingthe number of approaching AAs.

FIG. 12 illustrates the multiple possible trajectories 1215(1), 1215(2),1215(3), and 1215(4) (collectively “the possible trajectories 1215”)attacking agent 1210 may take while approaching protected site 1220. Thepossible trajectories 1215 may be predicted, using machine learningalgorithms executing on circuitry included at the BS-CC included inprotected site 1210 (e.g., BS-CC 132). To predict the possibletrajectories 1215 AA 1210 may traverse while approaching protected site1220, the artificial intelligence may use the propagation delay ofacoustic signals emitted from AA 1210, an angle(s) of arrival of AA1210, a speed profile(s) of AA 1210, and/or a combination thereof.

FIGS. 13A-13E depict AA multiple processes/groupings the ADAssurrounding a protected site may employ to detect an approachingattacking agent. Each of FIGS. 13A-13E illustrate a portion of protectedsite 1310 by six ADAs: 1320(1), 1320(2), 1320(3), 1320(3), 1320(4),1320(5), and 1320(6) (collectively “the ADAs 1320”). Also depicted ineach of the FIGS. 13A-13E is AA 1330 following a trajectory 1335 whileapproaching protected site 1310.

FIGS. 13A and 13B depict AA 1330 being tracked along flight path 1335 bya single group of three of the ADAs 1320 at a time. Initially, asdepicted in FIG. 13A, AA 1330 is detected by the group including ADAs1320(1), 1320(2), and 1320(3). As AA 1330 progresses along flight path1335, artificial intelligence executing on a BS-CC (not shown, howeverthe BS-CC included in 1310 may be substantially similar/identical toBS-CC 132) transmits a signal to ADA 1320(1) indicating ADA 1320(1) isno longer to detect AA 1330. Simultaneously, the BS-CC transmits asignal to ADA 1320(4) to begin detecting AA 1330 along with ADAs 1320(2)and 1320(3). FIG. 13B depicts AA 1330 being detected by ADAs 1320(2),1320(3), and 1320(4) after the artificial intelligence executing on theBS-CC included in protected site 1310 indicates a handoff between ADA1320(1) and 1320(4) is to be made.

Alternatively, as depicted in FIG. 13C, AA 1330 may be detected at thesame point along flight path 1335 by two overlapping groups of threeADAs at the same time. The first group of three ADAs detecting AA 1330in FIG. 13C includes ADAs 1320(1), 1320(2), and 1320(3) while the secondgroup of three ADAs detecting AA 1330 includes 1320(2), 1320(3), and1320(4). As noted above, if processing overhead permits, the BS-CC caninstruct all four ADAs (i.e., 1320(1), 1320(2), 1320(3), and 1320(4)) todetect AA 1330 simultaneously to increase the precision of themeasurements in some embodiments.

As AA 1330 continues to progress along flight path 1335, the computingdevice may determine it is more beneficial to have two distinct groupsof three ADAs detecting AA 1330 rather than two overlapping groups ofthree ADAs. FIG. 13D illustrates two distinct groups of three ADAsdetecting AA 1330 traversing flight path 1335. The first distinct groupof ADAs detecting AA 1330 includes ADAs 1320(1), 1320(2), and 1320(3),while the second distinct group includes ADAs 1320(4), 1320(5), and1320(6).

FIG. 13E depicts a scenario in which the computing device determines itis most beneficial for AA 1330 to be detected by as many groups of threeADAs surrounding protected 1310 as possible. As illustrated in FIG. 13E,four groups of three ADAs are detecting AA 1330 along flight path 1335simultaneously. The four groups of three ADAs detecting AA 1330 includea first group including ADAs 1320(1), 1320(2), and 1320(3); a secondgroup comprising ADAs 1320(2), 1320(3), and 1320(4); a third groupincluding ADAs 1320(3), 1320(4), and 1320(5); and a fourth groupincluding ADAs 1320(4), 1320(5), and 1320(6). The number of groups ofthree ADAs able to detect AA 1330 simultaneously may depend on thedistance of AA 1330 from the ADAs 1320, the strength of acousticsignal(s) emitted from AA 1330, the frequency with which each ADAdetects signals (i.e. localization frequency), and/or the sensitivity ofthe microphone array or other circuitry for sensing acoustic signalsincluded in the AAs 1320. As noted previously, according to certainembodiments all of the ADAs may be used simultaneously to detect anyincoming AA, which provides maximum precision in the localizationcalculations.

A first embodiment of the present disclosure is illustrated as shown inFIGS. 1-13. The first embodiment describes a distributed airborneacoustic anti-drone intelligence system (DAAADS) 100 for detectingtrajectories of unmanned aerial vehicles (UAV) (140(1), 140(2), 140(3))approaching a protected site 120, comprising a plurality of airbornedefense agents (ADAs) (110(1)-110(n)), wherein each ADA is located at afixed radius from the protected site and equidistant from each otherADA, wherein each ADA equipped with a directional microphone array 311configured to detect acoustic signals emitted by a UAV, a firstcomputing device 313 including a first computer-readable mediumcomprising first program instructions, executable by first processingcircuitry, to cause the first processing circuitry to determine adirection and a distance of each approaching UAV 140(1), 140(2), and140(3) from the ADA 110(1)-110(n), a GPS receiver 314 configured tolocate the ADA 110(1)-110(n) in three-dimensional space, a transceiver315, a base station 130 configured with a wideband communications link134 to communicate with the transceiver 315 of each ADA 110(1)-110(n)and the protected site 120, a control center (BS-CC) 132 located withinthe base station 130 and configured with a second computing device 136including a second computer-readable medium comprising programinstructions, executable by second processing circuitry, to cause thesecond processing circuitry to aggregate the directions and distances ofthe approaching UAVs 140(1), 140(2), and 140(3) to predict routestowards the protected site 120 and to alert the protected site 120 ofthe predicted route of each approaching UAV 140(1), 140(2), and 140(3).

Each ADA 110(1)-110(n) further comprises a motor 318, a propeller 319and navigation circuitry 316 configured to cause the ADA to hover at afixed height from the ground, at a fixed radius from the protected site120 and equidistant from each other ADA 110(1)-110(n).

The control center (BS-CC) 132 is configured to transmit positionsignals to the navigation circuitry 316 of each ADA 110(1)-110(n) tocontrol its speed and position.

DAAADS 100 further comprises a plurality of fixed towers 330 of heightH. Each tower 330 is located at a fixed radius from the protected site120 and equidistant from each other tower 330. DAAADS 100 furtherincludes a plurality of ropes, each rope 320 of length L, wherein a topportion of each fixed tower 330 is connected to one rope 320 of theplurality of ropes. A plurality of air balloons (not shown), each havinga lower mount attached to a rope 320, is configured to hold the firstcomputing device 313 and the directional microphone array 311 of an ADA(e.g., ADA 110(1)) at a fixed height of L+H above the ground.

In accordance with the present disclosure, the first computing device313 comprising DAAADS 100 includes switching circuitry configured toswitch the power of each directional microphone of directionalmicrophone array 311 ON and OFF in an alternating sequence. Moreover,the second processing circuitry 136 is configured to transmit controlsignals to the ADAs 110(1)-110(n) to switch the directional microphonearrays 311 ON and OFF simultaneously.

The first processing circuitry 313 may also be configured to: convertthe acoustic signals from the time domain to the frequency domain,identify a set of frequency components, and estimate the distance to thesound source.

In accordance with the present disclosure, each directional microphoneof the directional microphone array 311 is oriented to receive acousticsignals from a different angle. Each first computing device 313 includescircuitry configured to measure an amplitude, A, of each acoustic signalduring the alternating ON periods. Further, each first processingcircuitry 313 is configurable to detect an angle of arrival, ω, of theacoustic signal from the angle of the directional microphone receivingthe greatest amplitude, and to estimate the distance between each ADAand a UAV by measuring a propagation delay, τ, of the acoustic signalhaving the greatest amplitude.

The second processing circuitry 136 is configurable to predict the routeof an approaching UAV (e.g., 140(1)) by triangulating the acousticsignals received by the directional microphone arrays 311 of threeequidistant ADAs (e.g., 110(1), 110(2), and 110(3)). The secondprocessing circuitry further comprises a machine learning processorconfigured to predict the route of each approaching UAV. Secondprocessing circuitry 136 is further configurable to estimate the speedof the approaching UAV 140(1) by subtracting the distances estimated byeach of three equidistant ADAs 110(1), 110(2), and 110(3) at a first ONperiod (e.g., ON[1]) from the distances estimated at a second ON period(e.g., ON[2]) and dividing the difference by the time between the firstON period and the second ON period.

A second embodiment of the present disclosure depicted in FIGS. 1-13 isdirected to a method 400 for detecting trajectories of unmanned aerialvehicles (e.g., UAVs 140(1), 140(2), and 140(3)) approaching a protectedsite 120 by a plurality of airborne defense agents (ADAs 110(1)-110(n)).The method includes, at step 410, switching each directional microphoneof a directional microphone array 311 of an ADA ON and OFF duringconsecutive time periods in which only one directional microphone is ONin a time period. The method 400 further includes, at step 420,detecting acoustic signals generated by UAVs 140(1), 140(2), and 140(3)approaching the protected site 120 during consecutive ON periods. Method400 further comprises estimating, at step 430, by a first processingcircuitry 313 of an ADA (e.g., 110(1)), an angle of approach and adistance of each approaching UAV 140(1), 140(2), and 140(3) from eachADA 110(1)-110(n) during the first time period. At step 440, method 400comprises estimating an angle of approach and distance of eachapproaching UAV 140(1), 140(2), and 140(3) from each ADA 110(1)-110(n)during the second time period. The next step in method 400 includestransmitting the estimated angle of approach and distance from each ADA110(1)-110(n) to a base station 130 at step 450. At step 460, a secondprocessing circuitry 136 of the base station 130 estimates a speed ofeach UAV 140(1), 140(2), and 140(3) by subtracting the distanceestimated during a first ON time period from the distance measuredduring a second ON time period for each of three equidistant ADAs (e.g.,110(1), 110(2), and 110(3)) and dividing by the difference between thefirst and second time periods. Method 400 further includes, at step 470,aggregating the angles of approach, distances and speeds of theapproaching UAVs 140(1), 140(2), and 140(3) to predict routes towardsthe protected site 120. Method 400 concludes at step 480 by transmittingan alert to the protected site 120 when the route of at least oneapproaching UAV (e.g., 140(1), 140(2), or 140(3)) intersects with theprotected site 120.

Optionally, at step 405, navigation signals are transmitted to each ADA110(1)-110(n), wherein the navigation signals control the ADA110(1)-110(n) to hover at a fixed height from the ground, at a fixedradius from the protected site 120, and equidistant from each other ADAof the ADAs 110(1)-110(n). Also optionally, at step 408, the basestation 130 may transmit control signals to each ADA 110(1)-110(n) toswitch each directional microphone array 311 ON to start detectingacoustic signals or OFF to sleep based on a number of approaching UAVs.

In accordance with the present disclosure, the first processingcircuitry 313 of each ADA 110(1)-110(n) converts the acoustic signalsfrom the time domain into the frequency domain, identify a set offrequency components, and estimate the distance to the approaching UAV140(1), 140(2), and/or 140(3).

According to some aspects, each directional microphone of thedirectional microphone array (e.g., directional microphone array 311)may be oriented to receive acoustic signals from a different angle. Thefirst processing circuitry 313 measures an amplitude, A, of eachacoustic signal during the alternating ON periods. An angle of arrival,ω, of the acoustic signal is detected from the angle of the directionalmicrophone receiving the greatest amplitude. The distance between eachADA 110(1)-110(n) and a UAV 140(1), 140(2), and/or 140(3) is estimatedby measuring a propagation delay, τ, of the acoustic signal having thegreatest amplitude.

The second processing circuitry 136 predicts the route of an approachingUAV 140(1), 140(2), and/or 140(3) by triangulating the acoustic signalsreceived by the directional microphone arrays of three equidistant ADAs(e.g., 110(1), 110(2), and 110(3)). The second processing circuitry 136also applies the aggregated angles of approach, distances, and speeds ofthe approaching UAVs 140(1), 140(2), and 140(3) to a machine learningprocessor to predict the routes of the approaching UAVs 140(1), 140(2),and 140(3).

A third embodiment of the present disclosure depicted in FIGS. 1-13 isdirected to a non-transitory computer readable medium havinginstructions stored therein that, when executed by one or moreprocessors, cause the one or more processors to perform a method fordetecting trajectories of unmanned aerial vehicles (UAVs 140(1), 140(2),and 140(3)) approaching a protected site 120 by a plurality of airbornedefense agents (ADAs 110(1)-110(n)). The method executed comprisesswitching each directional microphone of a directional microphone array311 of an ADA (e.g., ADA 110(1)) ON and OFF during consecutive timeperiods in which only one directional microphone is ON in a time period.The method also includes detecting acoustic signals generated by UAVs140(1), 140(2), and 140(3) approaching the protected site 120 duringconsecutive ON periods; estimating, by a first processing circuitry 313of an ADA 110(1), an angle of approach and a distance of eachapproaching UAV 140(1), 140(2), and 140(3) from each ADA 110(1)-110(n)during the first time period; and estimating an angle of approach anddistance of each approaching UAV 140(1), 140(2), and 140(3) from eachADA 110(1)-110(n) during the second time period.

The method executed by the non-transitory computer readable mediumincludes transmitting the estimated angle of approach and distance fromeach ADA 110(1)-110(n) to a base station 130; estimating, by a secondprocessing circuitry 136 of the base station 130, a speed of each UAV140(1), 140(2), and 140(3) by subtracting the distance estimated duringa first ON time period (e.g., ON[1]) from the distance measured during asecond ON time period (e.g., ON[2]) for each of three equidistant ADAs(e.g., ADAs 110(1), 110(2), and 110(3) and dividing by the differencebetween the first and second time periods; aggregating the angles ofapproach, distances and speeds of the approaching UAVs 140(1), 140(2),and 140(3) to predict routes towards the protected site 120; andtransmitting an alert to the protected site 120 when the route of atleast one of the approaching UAVs 140(1), 140(2) and 140(3) intersectswith the protected site 120.

The non-transitory computer readable medium may further orient eachdirectional microphone of the directional microphone array 311 of an ADAof the ADAs 110(1)-110(n) to receive acoustic signals from a differentangle. The first processing circuitry 313 may also measure an amplitude,A, of each acoustic signal during the alternating ON periods; detect anangle of arrival, ω, of the acoustic signal from the angle of thedirectional microphone receiving the greatest amplitude; and estimatethe distance between each ADA 110(1)-110(n) and a UAV 140(1), 140(2),and/or 140(3) by measuring a propagation delay, τ, of the acousticsignal having the greatest amplitude.

Moreover, the non-transitory computer readable medium method is able topredict, by the second processing circuitry 136, the route of eachapproaching UAV by triangulating the acoustic signals received by thedirectional microphone arrays of three equidistant ADAs. The secondprocessing circuitry 136 also applies the aggregated angles of approach,distances, speeds, and predicted routes of the approaching UAVs 140(1),140(2), and 140(3) to a machine learning processor to predict the routeswhich intersect with the protected site 120.

Next, further details of the hardware description of the computingenvironment of base station 130 according to exemplary embodiments isdescribed with reference to FIG. 14. In FIG. 14, a controller 1400 isdescribed is representative of the computer 136 of FIG. 1 in which thecontroller is a computing device which includes a CPU 1401 whichperforms the processes described above/below. The process data andinstructions may be stored in memory 1402. These processes andinstructions may also be stored on a storage medium disk 1404 such as ahard drive (HDD) or portable storage medium or may be stored remotely.

Further, the claims are not limited by the form of the computer-readablemedia on which the instructions of the inventive process are stored. Forexample, the instructions may be stored on CDs, DVDs, in FLASH memory,RAM, ROM, PROM, EPROM, EEPROM, hard disk or any other informationprocessing device with which the computing device communicates, such asa server or computer.

Further, the claims may be provided as a utility application, backgrounddaemon, or component of an operating system, or combination thereof,executing in conjunction with CPU 1401, 1403 and an operating systemsuch as Microsoft Windows 7, Microsoft Windows 10, UNIX, Solaris, LINUX,Apple MAC-OS and other systems known to those skilled in the art.

The hardware elements in order to achieve the computing device may berealized by various circuitry elements, known to those skilled in theart. For example, CPU 1401 or CPU 1403 may be a Xenon or Core processorfrom Intel of America or an Opteron processor from AMD of America, ormay be other processor types that would be recognized by one of ordinaryskill in the art. Alternatively, the CPU 1401, 1403 may be implementedon an FPGA, ASIC, PLD or using discrete logic circuits, as one ofordinary skill in the art would recognize. Further, CPU 1401, 1403 maybe implemented as multiple processors cooperatively working in parallelto perform the instructions of the inventive processes described above.

The computing device in FIG. 14 also includes a network controller 1406,such as an Intel Ethernet PRO network interface card from IntelCorporation of America, for interfacing with network 1460. As can beappreciated, the network 1460 can be a public network, such as theInternet, or a private network such as an LAN or WAN network, or anycombination thereof and can also include PSTN or ISDN sub-networks. Thenetwork 1460 can also be wired, such as an Ethernet network, or can bewireless such as a cellular network including EDGE, 3G and 4G wirelesscellular systems. The wireless network can also be WiFi, Bluetooth, orany other wireless form of communication that is known.

The computing device further includes a display controller 1408, such asa NVIDIA GeForce GTX or Quadro graphics adaptor from NVIDIA Corporationof America for interfacing with display 1410, such as a Hewlett PackardHPL2445w LCD monitor. A general purpose I/O interface 1412 interfaceswith a keyboard and/or mouse 1414 as well as a touch screen panel 1416on or separate from display 1410. General purpose I/O interface alsoconnects to a variety of peripherals 1418 including printers andscanners, such as an OfficeJet or DeskJet from Hewlett Packard.

A sound controller 1420 is also provided in the computing device such asSound Blaster X-Fi Titanium from Creative, to interface withspeakers/microphone 1422 thereby providing sounds and/or music.

The general purpose storage controller 1424 connects the storage mediumdisk 1404 with communication bus 1426, which may be an ISA, EISA, VESA,PCI, or similar, for interconnecting all of the components of thecomputing device. A description of the general features andfunctionality of the display 1410, keyboard and/or mouse 1414, as wellas the display controller 1408, storage controller 1424, networkcontroller 1406, sound controller 1420, and general purpose I/Ointerface 1412 is omitted herein for brevity as these features areknown.

The exemplary circuit elements described in the context of the presentdisclosure may be replaced with other elements and structureddifferently than the examples provided herein. Moreover, circuitryconfigured to perform features described herein may be implemented inmultiple circuit units (e.g., chips), or the features may be combined incircuitry on a single chipset, as shown on FIG. 15.

FIG. 15 shows a schematic diagram of a data processing system, accordingto certain embodiments, for performing the functions of the exemplaryembodiments. The data processing system is an example of a computer inwhich code or instructions implementing the processes of theillustrative embodiments may be located.

In FIG. 15, data processing system 1500 employs a hub architectureincluding a north bridge and memory controller hub (NB/MCH) 1525 and asouth bridge and input/output (I/O) controller hub (SB/ICH) 1520. Thecentral processing unit (CPU) 1530 is connected to NB/MCH 1525. TheNB/MCH 1525 also connects to the memory 1545 via a memory bus, andconnects to the graphics processor 1550 via an accelerated graphics port(AGP). The NB/MCH 1525 also connects to the SB/ICH 1520 via an internalbus (e.g., a unified media interface or a direct media interface). TheCPU Processing unit 1530 may contain one or more processors and even maybe implemented using one or more heterogeneous processor systems.

For example, FIG. 16 shows one implementation of CPU 1530. In oneimplementation, the instruction register 1638 retrieves instructionsfrom the fast memory 1640. At least part of these instructions arefetched from the instruction register 1638 by the control logic 1636 andinterpreted according to the instruction set architecture of the CPU1530. Part of the instructions can also be directed to the register1632. In one implementation the instructions are decoded according to ahardwired method, and in another implementation the instructions aredecoded according a microprogram that translates instructions into setsof CPU configuration signals that are applied sequentially over multipleclock pulses. After fetching and decoding the instructions, theinstructions are executed using the arithmetic logic unit (ALU) 1634that loads values from the register 1632 and performs logical andmathematical operations on the loaded values according to theinstructions. The results from these operations can be feedback into theregister and/or stored in the fast memory 1640. According to certainimplementations, the instruction set architecture of the CPU 1530 canuse a reduced instruction set architecture, a complex instruction setarchitecture, a vector processor architecture, a very large instructionword architecture. Furthermore, the CPU 1530 can be based on the VonNeuman model or the Harvard model. The CPU 1530 can be a digital signalprocessor, an FPGA, an ASIC, a PLA, a PLD, or a CPLD. Further, the CPU1530 can be an x86 processor by Intel or by AMD; an ARM processor, aPower architecture processor by, e.g., IBM; a SPARC architectureprocessor by Sun Microsystems or by Oracle; or other known CPUarchitecture.

Referring again to FIG. 15, the data processing system 1500 can includethat the SB/ICH 1520 is coupled through a system bus to an I/O Bus, aread only memory (ROM) 1556, universal serial bus (USB) port 1564, aflash binary input/output system (BIOS) 1568, and a graphics controller1558. PCI/PCIe devices can also be coupled to SB/ICH 1588 through a PCIbus 1562.

The PCI devices may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. The Hard disk drive 1560 andCD-ROM 1566 can use, for example, an integrated drive electronics (IDE)or serial advanced technology attachment (SATA) interface. In oneimplementation the I/O bus can include a super I/O (SIO) device.

Further, the hard disk drive (HDD) 1560 and optical drive 1566 can alsobe coupled to the SB/ICH 1520 through a system bus. In oneimplementation, a keyboard 1570, a mouse 1572, a parallel port 1578, anda serial port 1576 can be connected to the system bus through the I/Obus. Other peripherals and devices that can be connected to the SB/ICH1520 using a mass storage controller such as SATA or PATA, an Ethernetport, an ISA bus, a LPC bridge, SMBus, a DMA controller, and an AudioCodec.

Moreover, the present disclosure is not limited to the specific circuitelements described herein, nor is the present disclosure limited to thespecific sizing and classification of these elements. For example, theskilled artisan will appreciate that the circuitry described herein maybe adapted based on changes on battery sizing and chemistry, or based onthe requirements of the intended back-up load to be powered.

The functions and features described herein may also be executed byvarious distributed components of a system. For example, one or moreprocessors may execute these system functions, wherein the processorsare distributed across multiple components communicating in a network.The distributed components may include one or more client and servermachines, which may share processing, as shown by FIG. 17, in additionto various human interface and communication devices (e.g., displaymonitors, smart phones, tablets, personal digital assistants (PDAs)).The network may be a private network, such as a LAN or WAN, or may be apublic network, such as the Internet. Input to the system may bereceived via direct user input and received remotely either in real-timeor as a batch process. Additionally, some implementations may beperformed on modules or hardware not identical to those described.Accordingly, other implementations are within the scope that may beclaimed.

The above-described hardware description is a non-limiting example ofcorresponding structure for performing the functionality describedherein.

Obviously, numerous modifications and variations of the presentdisclosure are possible in light of the above teachings. It is thereforeto be understood that within the scope of the appended claims, theinvention may be practiced otherwise than as specifically describedherein.

1-10. (canceled)
 11. A method to identify routes of unmanned aerialvehicles (UAV) approaching a protected site with a plurality of airborneagents and avoid intersection with the protected site, comprising:switching each directional microphone of a directional microphone arrayof a plurality of airborne defense agents (ADAs) ON and OFF duringconsecutive time periods in which only one directional microphone is ONin a time period, wherein each ADA is located at a fixed radius from theprotected site and equidistant from each other ADA; detecting acousticsignals generated by UAVs approaching the protected site duringconsecutive ON periods; estimating, by a first processing circuitry of afirst computing device of at least one of the ADAs, an angle of approachand a distance of each approaching UAV from each ADA during the firsttime period, wherein the first computing device includes a firstcomputer-readable medium comprising first program instructions,executable by the first processing circuitry, to cause the firstprocessing circuitry to determine a direction and a distance of eachapproaching UAV from the ADA; estimating an angle of approach and thedistance of each approaching UAV from each ADA during the second timeperiod; transmitting the estimated angle of approach and distance fromeach ADA to a base station; estimating, by a second processing circuitryof the base station, a speed of each UAV by subtracting the distanceestimated during a first ON time period from the distance measuredduring a second ON time period for each of three equidistant ADAs anddividing by the difference between the first and second time periods;aggregating, with the base station having a the control center, theangles of approach, distances and speeds of the approaching UAVs topredict routes towards the protected site, wherein the control center islocated within the base station and is configured with a secondcomputing device including a second computer-readable medium comprisingprogram instructions, executable by the second processing circuitry, tocause the second processing circuitry to aggregate the directions andthe distances of the approaching UAVs to predict routes towards theprotected site and to alert the protected site of the predicted route ofeach approaching UAV; transmitting an alert to the protected site whenthe route of at least one approaching UAV intersects with the protectedsite, and destroying the at least one approaching UAV to avoidintersection of the route of at least one approaching UAV with theprotected site, wherein each directional microphone array of theplurality of ADAs is held by an air balloon configured to hold the firstcomputing device at a height L+H above the ground, wherein H is a heightof a plurality of fixed towers, wherein each fixed tower of theplurality of fixed towers is located at a fixed radius from theprotected site and equidistant from each other fixed tower; wherein L isa length of each rope of a plurality of ropes, wherein a top portion ofeach fixed tower is connected to one rope of the plurality of ropes;wherein each air balloon has a lower mount attached to one rope of theplurality of ropes.
 12. The method of claim 11, further comprising:transmitting control signals from the base station to each ADA to switcheach directional microphone array ON to start detecting acoustic signalsor OFF to sleep based on a number of approaching UAVs.
 13. The method ofclaim 12, further comprising: transmitting navigation signals to eachADA, wherein the navigation signals control the ADA to hover at a fixedheight from the ground, at a fixed radius from the protected site andequidistant from each other ADA.
 14. The method of claim 12, furthercomprising: converting, by the first processing circuitry of each ADA,the acoustic signals from the time domain to the frequency domain;identifying a set of frequency components; and estimating the distanceto the approaching UAV. 15-20. (canceled)